"""simple docstring"""



import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


SCREAMING_SNAKE_CASE__:Optional[Any]									=			logging.get_logger(__name__)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""vocab_file""": """vocab.json""", """merges_file""": """merges.txt"""}

# See all BART models at https://huggingface.co/models?filter=bart
SCREAMING_SNAKE_CASE__:Optional[int]									=			{
    """vocab_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/vocab.json""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/vocab.json""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json""",
    },
    """merges_file""": {
        """facebook/bart-base""": """https://huggingface.co/facebook/bart-base/resolve/main/merges.txt""",
        """facebook/bart-large""": """https://huggingface.co/facebook/bart-large/resolve/main/merges.txt""",
        """facebook/bart-large-mnli""": """https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt""",
        """facebook/bart-large-cnn""": """https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt""",
        """facebook/bart-large-xsum""": """https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt""",
        """yjernite/bart_eli5""": """https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt""",
    },
}

SCREAMING_SNAKE_CASE__:Dict									=			{
    """facebook/bart-base""": 1024,
    """facebook/bart-large""": 1024,
    """facebook/bart-large-mnli""": 1024,
    """facebook/bart-large-cnn""": 1024,
    """facebook/bart-large-xsum""": 1024,
    """yjernite/bart_eli5""": 1024,
}



@lru_cache()
def 	_lowerCamelCase(	):
       __a       =    (
           list(range(ord("!"				)   ,    ord("~"				) + 1				)				) + list(range(ord("¡"				)   ,    ord("¬"				) + 1				)				) + list(range(ord("®"				)   ,    ord("ÿ"				) + 1				)				)
       )
       __a       =    bs[:]
       __a       =    0
       for b in range(2**8				):
              if b not in bs:
                     bs.append(a				)
                     cs.append(2**8 + n				)
                     n += 1
       __a       =    [chr(a				) for n in cs]
       return dict(zip(a   ,    a				)				)



def 	_lowerCamelCase(	a				):
       __a       =    set()
       __a       =    word[0]
       for char in word[1:]:
              pairs.add((prev_char, char)				)
              __a       =    char
       return pairs






class  snake_case__      (       snake_case_       ):
 _snake_case       :					Optional[int]							  =						VOCAB_FILES_NAMES
 _snake_case       :					Any							  =						PRETRAINED_VOCAB_FILES_MAP
 _snake_case       :					Optional[int]							  =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
 _snake_case       :					int							  =						["""input_ids""", """attention_mask"""]


 def __init__( self    ,		lowerCamelCase    ,		lowerCamelCase    ,		lowerCamelCase="replace"    ,		lowerCamelCase="<s>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="</s>"    ,		lowerCamelCase="<s>"    ,		lowerCamelCase="<unk>"    ,		lowerCamelCase="<pad>"    ,		lowerCamelCase="<mask>"    ,		lowerCamelCase=False    ,		**lowerCamelCase    ,		):
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else bos_token
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else eos_token
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else sep_token
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else cls_token
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else unk_token
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else pad_token

        # Mask token behave like a normal word, i.e. include the space before it
        __a       =    AddedToken(lowerCamelCase    ,		lstrip=lowerCamelCase    ,		rstrip=lowerCamelCase						) if isinstance(lowerCamelCase    ,		lowerCamelCase						) else mask_token

        super().__init__(
            errors=lowerCamelCase    ,		bos_token=lowerCamelCase    ,		eos_token=lowerCamelCase    ,		unk_token=lowerCamelCase    ,		sep_token=lowerCamelCase    ,		cls_token=lowerCamelCase    ,		pad_token=lowerCamelCase    ,		mask_token=lowerCamelCase    ,		add_prefix_space=lowerCamelCase    ,		**lowerCamelCase    ,		)

        with open(lowerCamelCase    ,		encoding="utf-8"						) as vocab_handle:
               __a       =    json.load(lowerCamelCase						)
        __a       =    {v: k for k, v in self.encoder.items()}
        __a       =    errors  # how to handle errors in decoding
        __a       =    bytes_to_unicode()
        __a       =    {v: k for k, v in self.byte_encoder.items()}
        with open(lowerCamelCase    ,		encoding="utf-8"						) as merges_handle:
               __a       =    merges_handle.read().split("\n"						)[1:-1]
        __a       =    [tuple(merge.split()						) for merge in bpe_merges]
        __a       =    dict(zip(lowerCamelCase    ,		range(len(lowerCamelCase						)						)						)						)
        __a       =    {}
        __a       =    add_prefix_space

        # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
        __a       =    re.compile(R"'s|'t|'re|'ve|'m|'ll|'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+"						)


 @property
 def 						a__   ( self						):
        return len(self.encoder						)


 def 						a__   ( self						):
        return dict(self.encoder    ,		**self.added_tokens_encoder						)


 def 						a__   ( self    ,		lowerCamelCase						):
        if token in self.cache:
               return self.cache[token]
        __a       =    tuple(lowerCamelCase						)
        __a       =    get_pairs(lowerCamelCase						)

        if not pairs:
               return token

        while True:
               __a       =    min(lowerCamelCase    ,		key=lambda lowerCamelCase						: self.bpe_ranks.get(lowerCamelCase    ,		float("inf"						)						)						)
               if bigram not in self.bpe_ranks:
                      break
               __a ,   __a       =    bigram
               __a       =    []
               __a       =    0
               while i < len(lowerCamelCase						):
                      try:
                             __a       =    word.index(lowerCamelCase    ,		lowerCamelCase						)
                      except ValueError:
                             new_word.extend(word[i:]						)
                             break
                      else:
                             new_word.extend(word[i:j]						)
                             __a       =    j

                      if word[i] == first and i < len(lowerCamelCase						) - 1 and word[i + 1] == second:
                             new_word.append(first + second						)
                             i += 2
                      else:
                             new_word.append(word[i]						)
                             i += 1
               __a       =    tuple(lowerCamelCase						)
               __a       =    new_word
               if len(lowerCamelCase						) == 1:
                      break
               else:
                      __a       =    get_pairs(lowerCamelCase						)
        __a       =    " ".join(lowerCamelCase						)
        __a       =    word
        return word


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    []
        for token in re.findall(self.pat    ,		lowerCamelCase						):
               __a       =    "".join(
                   self.byte_encoder[b] for b in token.encode("utf-8"						)						)  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
               bpe_tokens.extend(bpe_token for bpe_token in self.bpe(lowerCamelCase						).split(" "						)						)
        return bpe_tokens


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.encoder.get(lowerCamelCase    ,		self.encoder.get(self.unk_token						)						)


 def 						a__   ( self    ,		lowerCamelCase						):
        return self.decoder.get(lowerCamelCase						)


 def 						a__   ( self    ,		lowerCamelCase						):
        __a       =    "".join(lowerCamelCase						)
        __a       =    bytearray([self.byte_decoder[c] for c in text]						).decode("utf-8"    ,		errors=self.errors						)
        return text


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        if not os.path.isdir(lowerCamelCase						):
               logger.error(F"Vocabulary path ({save_directory}) should be a directory"						)
               return
        __a       =    os.path.join(
            lowerCamelCase    ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["vocab_file"]						)
        __a       =    os.path.join(
            lowerCamelCase    ,		(filename_prefix + "-" if filename_prefix else "") + VOCAB_FILES_NAMES["merges_file"]						)

        with open(lowerCamelCase    ,		"w"    ,		encoding="utf-8"						) as f:
               f.write(json.dumps(self.encoder    ,		indent=2    ,		sort_keys=lowerCamelCase    ,		ensure_ascii=lowerCamelCase						) + "\n"						)

        __a       =    0
        with open(lowerCamelCase    ,		"w"    ,		encoding="utf-8"						) as writer:
               writer.write("#version: 0.2\n"						)
               for bpe_tokens, token_index in sorted(self.bpe_ranks.items()    ,		key=lambda lowerCamelCase						: kv[1]						):
                      if index != token_index:
                             logger.warning(
                                 F"Saving vocabulary to {merge_file}: BPE merge indices are not consecutive."
                                 " Please check that the tokenizer is not corrupted!"						)
                             __a       =    token_index
                      writer.write(" ".join(lowerCamelCase						) + "\n"						)
                      index += 1

        return vocab_file, merge_file


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        if token_ids_a is None:
               return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
        __a       =    [self.cls_token_id]
        __a       =    [self.sep_token_id]
        return cls + token_ids_a + sep + sep + token_ids_a + sep


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None    ,		lowerCamelCase = False						):
        if already_has_special_tokens:
               return super().get_special_tokens_mask(
                   token_ids_a=lowerCamelCase    ,		token_ids_a=lowerCamelCase    ,		already_has_special_tokens=lowerCamelCase						)

        if token_ids_a is None:
               return [1] + ([0] * len(lowerCamelCase						)) + [1]
        return [1] + ([0] * len(lowerCamelCase						)) + [1, 1] + ([0] * len(lowerCamelCase						)) + [1]


 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase = None						):
        __a       =    [self.sep_token_id]
        __a       =    [self.cls_token_id]

        if token_ids_a is None:
               return len(cls + token_ids_a + sep						) * [0]
        return len(cls + token_ids_a + sep + sep + token_ids_a + sep						) * [0]






 def 						a__   ( self    ,		lowerCamelCase    ,		lowerCamelCase=False    ,		**lowerCamelCase						):
        __a       =    kwargs.pop("add_prefix_space"    ,		self.add_prefix_space						)
        if (is_split_into_words or add_prefix_space) and (len(lowerCamelCase						) > 0 and not text[0].isspace()):
               __a       =    " " + text
        return (text, kwargs)

"""simple docstring"""



import argparse
import collections
import os
import re
import tempfile

import pandas as pd
from datasets import Dataset
from huggingface_hub import hf_hub_download, upload_folder

from transformers.utils import direct_transformers_import


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/update_metadata.py
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			"""src/transformers"""


# This is to make sure the transformers module imported is the one in the repo.
SCREAMING_SNAKE_CASE__:List[str]									=			direct_transformers_import(TRANSFORMERS_PATH)


# Regexes that match TF/Flax/PT model names.
SCREAMING_SNAKE_CASE__:Union[str, Any]									=			re.compile(R"""TF(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
SCREAMING_SNAKE_CASE__:Optional[int]									=			re.compile(R"""Flax(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")
# Will match any TF or Flax model too so need to be in an else branch afterthe two previous regexes.
SCREAMING_SNAKE_CASE__:Optional[Any]									=			re.compile(R"""(.*)(?:Model|Encoder|Decoder|ForConditionalGeneration)""")


# Fill this with tuples (pipeline_tag, model_mapping, auto_model)
SCREAMING_SNAKE_CASE__:int									=			[
    ("""pretraining""", """MODEL_FOR_PRETRAINING_MAPPING_NAMES""", """AutoModelForPreTraining"""),
    ("""feature-extraction""", """MODEL_MAPPING_NAMES""", """AutoModel"""),
    ("""audio-classification""", """MODEL_FOR_AUDIO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForAudioClassification"""),
    ("""text-generation""", """MODEL_FOR_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForCausalLM"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_CTC_MAPPING_NAMES""", """AutoModelForCTC"""),
    ("""image-classification""", """MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForImageClassification"""),
    ("""image-segmentation""", """MODEL_FOR_IMAGE_SEGMENTATION_MAPPING_NAMES""", """AutoModelForImageSegmentation"""),
    ("""fill-mask""", """MODEL_FOR_MASKED_LM_MAPPING_NAMES""", """AutoModelForMaskedLM"""),
    ("""object-detection""", """MODEL_FOR_OBJECT_DETECTION_MAPPING_NAMES""", """AutoModelForObjectDetection"""),
    (
        """zero-shot-object-detection""",
        """MODEL_FOR_ZERO_SHOT_OBJECT_DETECTION_MAPPING_NAMES""",
        """AutoModelForZeroShotObjectDetection""",
    ),
    ("""question-answering""", """MODEL_FOR_QUESTION_ANSWERING_MAPPING_NAMES""", """AutoModelForQuestionAnswering"""),
    ("""text2text-generation""", """MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES""", """AutoModelForSeq2SeqLM"""),
    ("""text-classification""", """MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForSequenceClassification"""),
    ("""automatic-speech-recognition""", """MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING_NAMES""", """AutoModelForSpeechSeq2Seq"""),
    (
        """table-question-answering""",
        """MODEL_FOR_TABLE_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForTableQuestionAnswering""",
    ),
    ("""token-classification""", """MODEL_FOR_TOKEN_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForTokenClassification"""),
    ("""multiple-choice""", """MODEL_FOR_MULTIPLE_CHOICE_MAPPING_NAMES""", """AutoModelForMultipleChoice"""),
    (
        """next-sentence-prediction""",
        """MODEL_FOR_NEXT_SENTENCE_PREDICTION_MAPPING_NAMES""",
        """AutoModelForNextSentencePrediction""",
    ),
    (
        """audio-frame-classification""",
        """MODEL_FOR_AUDIO_FRAME_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForAudioFrameClassification""",
    ),
    ("""audio-xvector""", """MODEL_FOR_AUDIO_XVECTOR_MAPPING_NAMES""", """AutoModelForAudioXVector"""),
    (
        """document-question-answering""",
        """MODEL_FOR_DOCUMENT_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForDocumentQuestionAnswering""",
    ),
    (
        """visual-question-answering""",
        """MODEL_FOR_VISUAL_QUESTION_ANSWERING_MAPPING_NAMES""",
        """AutoModelForVisualQuestionAnswering""",
    ),
    ("""image-to-text""", """MODEL_FOR_FOR_VISION_2_SEQ_MAPPING_NAMES""", """AutoModelForVision2Seq"""),
    (
        """zero-shot-image-classification""",
        """MODEL_FOR_ZERO_SHOT_IMAGE_CLASSIFICATION_MAPPING_NAMES""",
        """AutoModelForZeroShotImageClassification""",
    ),
    ("""depth-estimation""", """MODEL_FOR_DEPTH_ESTIMATION_MAPPING_NAMES""", """AutoModelForDepthEstimation"""),
    ("""video-classification""", """MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING_NAMES""", """AutoModelForVideoClassification"""),
    ("""mask-generation""", """MODEL_FOR_MASK_GENERATION_MAPPING_NAMES""", """AutoModelForMaskGeneration"""),
]



def 	_lowerCamelCase(	a				):
       __a       =    re.finditer(".+?(?:(?<=[a-z])(?=[A-Z])|(?<=[A-Z])(?=[A-Z][a-z])|$)"   ,    a				)
       return [m.group(0				) for m in matches]



def 	_lowerCamelCase(	):
       __a       =    transformers_module.models.auto.configuration_auto.CONFIG_MAPPING_NAMES
       __a       =    {
           config.replace("Config"   ,    ""				): model_type for model_type, config in config_maping_names.items()
       }

       # Dictionaries flagging if each model prefix has a backend in PT/TF/Flax.
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)
       __a       =    collections.defaultdict(a				)

       # Let's lookup through all transformers object (once) and find if models are supported by a given backend.
       for attr_name in dir(a				):
              __a       =    None
              if _re_tf_models.match(a				) is not None:
                     __a       =    tf_models
                     __a       =    _re_tf_models.match(a				).groups()[0]
              elif _re_flax_models.match(a				) is not None:
                     __a       =    flax_models
                     __a       =    _re_flax_models.match(a				).groups()[0]
              elif _re_pt_models.match(a				) is not None:
                     __a       =    pt_models
                     __a       =    _re_pt_models.match(a				).groups()[0]

              if lookup_dict is not None:
                     while len(a				) > 0:
                            if attr_name in model_prefix_to_model_type:
                                   __a       =    True
                                   break
                            # Try again after removing the last word in the name
                            __a       =    "".join(camel_case_split(a				)[:-1]				)

       __a       =    set(list(pt_models.keys()				) + list(tf_models.keys()				) + list(flax_models.keys()				)				)
       __a       =    list(a				)
       all_models.sort()

       __a       =    {"model_type": all_models}
       __a       =    [pt_models[t] for t in all_models]
       __a       =    [tf_models[t] for t in all_models]
       __a       =    [flax_models[t] for t in all_models]

       # Now let's use the auto-mapping names to make sure
       __a       =    {}
       for t in all_models:
              if t in transformers_module.models.auto.processing_auto.PROCESSOR_MAPPING_NAMES:
                     __a       =    "AutoProcessor"
              elif t in transformers_module.models.auto.tokenization_auto.TOKENIZER_MAPPING_NAMES:
                     __a       =    "AutoTokenizer"
              elif t in transformers_module.models.auto.feature_extraction_auto.FEATURE_EXTRACTOR_MAPPING_NAMES:
                     __a       =    "AutoFeatureExtractor"
              else:
                     # Default to AutoTokenizer if a model has nothing, for backward compatibility.
                     __a       =    "AutoTokenizer"

       __a       =    [processors[t] for t in all_models]

       return pd.DataFrame(a				)



def 	_lowerCamelCase(	a				):
       __a       =    [
           transformers_module.models.auto.modeling_auto,
           transformers_module.models.auto.modeling_tf_auto,
           transformers_module.models.auto.modeling_flax_auto,
       ]
       for pipeline_tag, model_mapping, auto_class in PIPELINE_TAGS_AND_AUTO_MODELS:
              __a       =    [model_mapping, F"TF_{model_mapping}", F"FLAX_{model_mapping}"]
              __a       =    [auto_class, F"TF_{auto_class}", F"Flax_{auto_class}"]
              # Loop through all three frameworks
              for module, cls, mapping in zip(a   ,    a   ,    a				):
                     # The type of pipeline may not exist in this framework
                     if not hasattr(a   ,    a				):
                            continue
                     # First extract all model_names
                     __a       =    []
                     for name in getattr(a   ,    a				).values():
                            if isinstance(a   ,    a				):
                                   model_names.append(a				)
                            else:
                                   model_names.extend(list(a				)				)

            # Add pipeline tag and auto model class for those models
                     table.update({model_name: (pipeline_tag, cls) for model_name in model_names}				)

       return table



def 	_lowerCamelCase(	a   ,    a				):
       __a       =    get_frameworks_table()
       __a       =    Dataset.from_pandas(a				)

       __a       =    hf_hub_download(
           "huggingface/transformers-metadata"   ,    "pipeline_tags.json"   ,    repo_type="dataset"   ,    token=a				)
       __a       =    Dataset.from_json(a				)
       __a       =    {
           tags_dataset[i]["model_class"]: (tags_dataset[i]["pipeline_tag"], tags_dataset[i]["auto_class"])
           for i in range(len(a				)				)
       }
       __a       =    update_pipeline_and_auto_class_table(a				)

       # Sort the model classes to avoid some nondeterministic updates to create false update commits.
       __a       =    sorted(table.keys()				)
       __a       =    pd.DataFrame(
           {
               "model_class": model_classes,
               "pipeline_tag": [table[m][0] for m in model_classes],
               "auto_class": [table[m][1] for m in model_classes],
           }				)
       __a       =    Dataset.from_pandas(a				)

       with tempfile.TemporaryDirectory() as tmp_dir:
              frameworks_dataset.to_json(os.path.join(a   ,    "frameworks.json"				)				)
              tags_dataset.to_json(os.path.join(a   ,    "pipeline_tags.json"				)				)

              if commit_sha is not None:
                     __a       =    (
                         F"Update with commit {commit_sha}\n\nSee: "
                         F"https://github.com/huggingface/transformers/commit/{commit_sha}"
                     )
              else:
                     __a       =    "Update"

              upload_folder(
                  repo_id="huggingface/transformers-metadata"   ,    folder_path=a   ,    repo_type="dataset"   ,    token=a   ,    commit_message=a   ,    )



def 	_lowerCamelCase(	):
       __a       =    {tag: cls for tag, _, cls in PIPELINE_TAGS_AND_AUTO_MODELS}
       __a       =    transformers_module.pipelines.SUPPORTED_TASKS
       __a       =    []
       for key in pipeline_tasks:
              if key not in in_table:
                     __a       =    pipeline_tasks[key]["pt"]
                     if isinstance(a   ,    (list, tuple)				):
                            __a       =    model[0]
                     __a       =    model.__name__
                     if model not in in_table.values():
                            missing.append(a				)

       if len(a				) > 0:
              __a       =    ", ".join(a				)
              raise ValueError(
                  "The following pipeline tags are not present in the `PIPELINE_TAGS_AND_AUTO_MODELS` constant inside "
                  F"`utils/update_metadata.py`: {msg}. Please add them!"				)


if __name__ == "__main__":
   SCREAMING_SNAKE_CASE__:List[str]									=			argparse.ArgumentParser()
   parser.add_argument("""--token""", type=str, help="""The token to use to push to the transformers-metadata dataset.""")
   parser.add_argument("""--commit_sha""", type=str, help="""The sha of the commit going with this update.""")
   parser.add_argument("""--check-only""", action="""store_true""", help="""Activate to just check all pipelines are present.""")
   SCREAMING_SNAKE_CASE__:Any									=			parser.parse_args()

   if args.check_only:
      check_pipeline_tags()
   else:
      update_metadata(args.token, args.commit_sha)
